Two preprints on Conn. NLP

[Gabriele Scheler]

Dear connectionists,
two preprints on connectionist and hybrid approaches to NLP 
are available from our ftp-server.

ftp flop.informatik.tu-muenchen.de
directory: pub/articles-etc
scheler_aspect.ps.gz
scheler_hybrid.ps.gz

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Dr. Gabriele Scheler				phone: +49-89-2105-8476
Institut f"ur Informatik			fax:   +49-89-2105-8207
Technische Universit"at M"unchen
Arcisstr.21				email:scheler at informatik.tu-muenchen.de
D-82090 M"unchen
Germany
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Titles and Abstracts:
scheler_hybrid.ps.gz
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A Hybrid Model of Semantic Inference

(to appear:
 4th Conference on Cognitive Science in Natural Language Processing}, 
Dublin, Ireland, 1995 )

Gabriele Scheler/Johann Schumann
Institut f{\"u}r Informatik
Technische Universit{\"a}t M{\"u}nchen
e-mail: scheler, schumann at informatik.tu-muenchen.de

keywords: NLP, hybrid systems, automated theorem proving,
text understanding, temporal cognition

Abstract (Conclusion)

We have built a system for the interpretation of tense and aspects
consisting of a neural network component, which translates sentences 
into semantic representations and a theorem prover which proves 
inferences between logical forms. 

An interesting question that has been partly answered by this research 
is whether atomic features can indeed mediate between syntactic 
structure and cognitive structure, which are both complex.
It was shown that complex logical representations can be built from
an atomic feature representation.  

The implications for learning of natural language categories and 
the interface between natural language and cognition from this 
approach could be far-reaching. 
For instance, atomic features may be seen as a biologically simple 
way of linking structures (i.e. natural language morphology and 
temporal cognition) which are complex in different ways.

However, this approach needs to be carried over to other linguistic
domains (e.g. determiners, plural phenomena, mood, prepositions or 
lexical meaning) to further explore its possibilities. 

Logic is probably not the implementation medium in human brains. 
Logical representations must be seen to provide only a meta-theory 
of cognition. 
In contrast to other approaches, we use full first-order logic
as representation medium. This allows an open set of inferences 
which can be applied to various tasks. These inferences can also 
be used to construct a closed relational set, i.e. a temporal 
'scenario'. 

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scheler_aspect.ps.gz

Learning the Semantics of Aspect
(to appear: D. Jones (ed.) New Methods in Language Processing,
University College London Press)

Gabriele Scheler

The main point of this paper is to show how we can extract semantic features, 
describing aspectual meanings, from a syntactic representation. 
Aspectual meanings are represented as sets of features in an interlingua.
The goal is to translate English to Russian aspectual categories.
This is realized by a specialized language processing module, which is based
on the concept of vertical modularity.
The results of supervised learning of syntactic-semantic correspondences
using standard back-propagation show that both learning and generalization 
to new patterns are successful.
Furthermore, the correct generation of Russian aspect from the automatically 
created semantic representations is demonstrated. 
The results are relevant to machine translation in a hybrid systems approach 
and to the study of linguistic category formation.
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